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"""The embedding model base class."""
from __future__ import annotations
import asyncio
import inspect
from abc import abstractmethod
from pathlib import Path
from typing import Any, Generic, TypeVar, Type, Union
from pydantic import BaseModel, ConfigDict
from ._embedding_model_card import EmbeddingModelCard
from ._embedding_response import EmbeddingResponse
from ._embedding_usage import EmbeddingUsage
from .._logging import logger
from ..credential import CredentialBase
from ..message import DataBlock, TextBlock
#: Type variable for embedding input elements.
#:
#: Bound to the union of all element shapes the framework supports;
#: each concrete subclass narrows it to its accepted input via
#: ``class Foo(EmbeddingModelBase[str | TextBlock]): ...`` so callers
#: get accurate IDE completion and type-checking on ``__call__``.
#:
#: ``__call__`` accepts ``TextBlock`` and unpacks it to ``.text``
#: before invoking :meth:`_call_api`; therefore ``_call_api``'s input
#: type is intentionally decoupled from :data:`InputT` (it is typed
#: ``list[Any]`` on the base and narrowed by each subclass).
InputT = TypeVar("InputT", bound=Union[str, TextBlock, DataBlock])
class EmbeddingModelBase(Generic[InputT]):
"""Base class for embedding models.
Generic over :data:`InputT` so that text-only subclasses
(``EmbeddingModelBase[str]``) and multimodal subclasses
(``EmbeddingModelBase[str | TextBlock | DataBlock]``) expose the
correct ``inputs`` type to the IDE.
Follows the same pattern as :class:`~agentscope.model.ChatModelBase`:
- ``__call__`` splits inputs into batches of size
:attr:`batch_size`, calls :meth:`_call_api` for each batch
**concurrently** via :func:`asyncio.gather`, and merges the
results. Each batch call is wrapped with retry logic.
- Subclasses only implement :meth:`_call_api` for a **single
batch** — no batching or retry code needed.
- Each subclass may override :meth:`_get_retryable_exceptions` to
declare provider-specific retriable errors.
"""
class Parameters(BaseModel):
"""Provider-specific tunables for embedding models.
Intentionally empty in the base — ``dimensions`` is a contract
property of an embedding model (its output vector size), not a
tunable knob, so it lives directly on the instance via the
required :paramref:`__init__.dimensions` argument. Subclasses
extend this class to expose **non-dimensional** knobs (e.g.
Gemini's ``task_type``, Dashscope's ``text_type``).
``extra="allow"`` is set so old persisted configs (where
``dimensions`` lived in ``parameters``) keep deserialising
without raising; :meth:`EmbeddingModelBase.__init__` extracts
it back out for backward compatibility.
"""
model_config = ConfigDict(extra="allow")
credential: CredentialBase
"""The API credential."""
model: str
"""The embedding model name."""
dimensions: int
"""The output embedding vector dimensions.
Set directly from the :paramref:`__init__.dimensions` argument —
a required, first-class field rather than something derived from
:attr:`parameters`.
"""
context_size: int
"""Maximum input length (in tokens) per single input item."""
batch_size: int
"""Maximum number of input items per API call."""
max_retries: int
"""The maximum number of retries for the underlying API."""
retry_delay: float
"""Seconds to sleep between retry attempts."""
supports_multimodal: bool = False
"""Whether this model instance accepts :class:`DataBlock` inputs in
addition to text. Text-only models keep the default ``False``;
multimodal subclasses must set it to ``True`` (per instance when
routing depends on the model name)."""
def __init__(
self,
credential: CredentialBase,
model: str,
dimensions: int | None,
parameters: BaseModel | None,
context_size: int,
batch_size: int,
max_retries: int,
retry_delay: float,
) -> None:
"""Initialize the embedding model base class.
Args:
credential (`CredentialBase`):
The API credential used for authentication.
model (`str`):
The name of the embedding model.
dimensions (`int | None`):
The output embedding vector dimensions for this
instance. Required and first-class — see the class
docstring for the rationale of keeping ``dimensions``
outside :class:`Parameters`. For backward compatibility
with older configs that stored ``dimensions`` inside
:class:`Parameters`, ``None`` is accepted at the
signature level and is back-filled from
``parameters.dimensions`` when present.
parameters (`BaseModel | None`):
Provider-specific non-dimensional parameters. When
``None``, the default ``Parameters()`` is used.
context_size (`int`):
Maximum input length (in tokens) per single input item.
batch_size (`int`):
Maximum number of input items per API call. When
``__call__`` receives more items, it splits them into
batches and calls :meth:`_call_api` concurrently.
max_retries (`int`):
The maximum number of retries for each batch API call.
Only exceptions listed in
:meth:`_get_retryable_exceptions` count against this
budget.
retry_delay (`float`):
Seconds to sleep between retry attempts.
"""
resolved_parameters = parameters or self.Parameters()
# Backward-compat: older session/KB configs persisted
# ``dimensions`` inside ``parameters``. Promote it to the
# constructor argument when the caller did not pass one
# explicitly, then strip it from the parameters object so it
# never reaches provider-specific request payloads.
param_dump = resolved_parameters.model_dump()
legacy_dimensions = param_dump.pop("dimensions", None)
if dimensions is None:
if legacy_dimensions is None:
raise ValueError(
"dimensions is required: pass it explicitly to "
"EmbeddingModelBase.__init__ or include it in the "
"legacy `parameters` mapping.",
)
dimensions = int(legacy_dimensions)
resolved_parameters = type(resolved_parameters)(**param_dump)
elif legacy_dimensions is not None:
# Both routes set it — explicit constructor wins, strip the
# legacy mirror so it can't drift.
resolved_parameters = type(resolved_parameters)(**param_dump)
if dimensions <= 0:
raise ValueError(
f"dimensions must be a positive integer, got {dimensions}.",
)
self.credential = credential
self.model = model
self.dimensions = dimensions
self.parameters = resolved_parameters
self.context_size = context_size
self.batch_size = batch_size
self.max_retries = max_retries
self.retry_delay = retry_delay
@classmethod
def _get_retryable_exceptions(cls) -> tuple[Type[Exception], ...]:
"""Return exception types that should trigger a retry.
Defaults to an empty tuple (no retries). Subclasses can
override to declare provider-specific retryable exceptions.
"""
return ()
# ------------------------------------------------------------------
# Public API — batching + concurrent retry
# ------------------------------------------------------------------
async def __call__(
self,
inputs: list[InputT],
**kwargs: Any,
) -> EmbeddingResponse:
"""Embed a list of inputs with automatic batching and retry.
The inputs are split into chunks of :attr:`batch_size`. All
chunks are dispatched **concurrently** via
:func:`asyncio.gather`. Each chunk is individually retried up
to ``max_retries`` times on retryable errors. Results are
merged into a single :class:`EmbeddingResponse` preserving the
original input order.
Args:
inputs (`list[InputT]`):
The input data to embed. For text-only models this is
``list[str]``; for multimodal models it is
``list[str | TextBlock | DataBlock]``. Any
:class:`TextBlock` items are transparently unpacked to
their ``.text`` field on entry, so subclasses' batching
and ``_call_api`` only have to handle ``str`` (and
``DataBlock`` for multimodal variants).
**kwargs:
Additional keyword arguments forwarded to
:meth:`_call_api`.
Returns:
`EmbeddingResponse`:
A merged response containing embeddings for all inputs.
"""
if not inputs:
return EmbeddingResponse(
embeddings=[],
usage=EmbeddingUsage(tokens=0, time=0),
)
normalized: list[Any] = [
item.text if isinstance(item, TextBlock) else item
for item in inputs
]
# Split into batches.
batches = [
normalized[i : i + self.batch_size]
for i in range(0, len(normalized), self.batch_size)
]
if len(batches) > 1:
logger.info(
"Embedding %d inputs in %d batches (batch_size=%d) "
"for model %s.",
len(normalized),
len(batches),
self.batch_size,
self.model,
)
# Dispatch all batches concurrently, each with retry.
results: list[EmbeddingResponse] = await asyncio.gather(
*(self._call_with_retry(batch, **kwargs) for batch in batches),
)
return self._merge_responses(results)
# ------------------------------------------------------------------
# Internal — merge multiple batch responses
# ------------------------------------------------------------------
@staticmethod
def _merge_responses(
responses: list[EmbeddingResponse],
) -> EmbeddingResponse:
"""Merge multiple batch :class:`EmbeddingResponse` objects into
one, preserving input order.
Args:
responses (`list[EmbeddingResponse]`):
Batch responses to merge.
Returns:
`EmbeddingResponse`: The merged response.
"""
if len(responses) == 1:
return responses[0]
all_embeddings: list = []
total_tokens = 0
total_time = 0.0
for resp in responses:
all_embeddings.extend(resp.embeddings)
if resp.usage:
total_time += resp.usage.time
if resp.usage.tokens:
total_tokens += resp.usage.tokens
return EmbeddingResponse(
embeddings=all_embeddings,
usage=EmbeddingUsage(
tokens=total_tokens,
time=total_time,
),
source="api",
)
# ------------------------------------------------------------------
# Internal — retry wrapper for a single batch
# ------------------------------------------------------------------
async def _call_with_retry(
self,
inputs: list[Any],
**kwargs: Any,
) -> EmbeddingResponse:
"""Call :meth:`_call_api` with retry logic for a single batch.
Args:
inputs (`list[Any]`):
A single batch of inputs (size ≤ ``batch_size``), already
normalised by :meth:`__call__` (any :class:`TextBlock`
items unpacked to their ``.text``). Typed as
``list[Any]`` because the concrete element shape depends
on the subclass — see :meth:`_call_api`.
**kwargs:
Forwarded to :meth:`_call_api`.
"""
retryable = tuple(self._get_retryable_exceptions())
last_error: Exception | None = None
for attempt in range(self.max_retries + 1):
try:
return await self._call_api(inputs, **kwargs)
except Exception as e:
if not isinstance(e, retryable):
raise
last_error = e
if attempt < self.max_retries:
logger.warning(
"Batch attempt %d failed for embedding model "
"%s: %s. Retrying in %.1fs...",
attempt + 1,
self.model,
str(e),
self.retry_delay,
)
await asyncio.sleep(self.retry_delay)
else:
logger.warning(
"All %d attempt(s) failed for a batch of "
"embedding model %s.",
self.max_retries + 1,
self.model,
)
if last_error is not None:
raise last_error
raise RuntimeError(
f"Failed to call embedding model {self.model} after "
f"{self.max_retries + 1} retries.",
)
# ------------------------------------------------------------------
# Abstract — subclasses implement this for a single batch
# ------------------------------------------------------------------
@abstractmethod
async def _call_api(
self,
inputs: list[Any],
**kwargs: Any,
) -> EmbeddingResponse:
"""Call the underlying embedding API for a **single batch**.
Subclasses must implement this method. The batch splitting,
concurrency, and retry logic are handled by :meth:`__call__`
— this method only needs to handle one API call.
.. note::
The parameter is typed ``list[Any]`` rather than
``list[InputT]`` because :meth:`__call__` unpacks
:class:`TextBlock` items to their ``.text`` field *before*
dispatching to this method. The element shape this method
actually receives is therefore subclass-specific
(:data:`InputT` minus :class:`TextBlock`). Subclasses
should override with their concrete narrower type, e.g.
``list[str]`` for text-only models or
``list[str | DataBlock]`` for multimodal ones.
Args:
inputs (`list[Any]`):
A batch of inputs (guaranteed ``len(inputs) <=
self.batch_size``).
**kwargs:
Additional keyword arguments.
Returns:
`EmbeddingResponse`:
The embedding response for this batch.
"""
# ------------------------------------------------------------------
# Model card discovery
# ------------------------------------------------------------------
@classmethod
def list_models(
cls,
custom_yaml_dir: str | None = None,
) -> list[EmbeddingModelCard]:
"""List candidate embedding models from YAML files.
Each concrete subclass should live in its own provider
subdirectory (e.g. ``embedding/_openai/_model.py``) with a
sibling ``_models/`` directory containing YAML files — identical
to the layout used by :class:`~agentscope.model.ChatModelBase`.
Args:
custom_yaml_dir (`str | None`):
Override the YAML directory.
Returns:
`list[EmbeddingModelCard]`:
A list of embedding model cards.
"""
if custom_yaml_dir is None:
subclass_file = Path(inspect.getfile(cls))
yaml_dir = subclass_file.parent / "_models"
else:
yaml_dir = Path(custom_yaml_dir)
if not yaml_dir.is_dir():
return []
yaml_files = list(yaml_dir.glob("*.yaml"))
model_cards = []
for yaml_file in yaml_files:
try:
card = EmbeddingModelCard.from_yaml(
yaml_path=str(yaml_file),
parameter_class=cls.Parameters,
)
model_cards.append(card)
except Exception as e:
logger.warning(
"Failed to load embedding model card %s: %s",
yaml_file,
str(e),
)
continue
return model_cards
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